Follow the actual loop
Prepare for the coding, math, system-design, research, product, project, and strategy rounds recruiting confirmed.
Free senior ML interview field guide
Prepare for the ML interview you actually have.
Learn what strong answers require for Applied Scientist, Research Scientist, Machine Learning Engineer, and Research Engineer loops. Study in order, practice under time, and go deep enough for senior through principal scope.
- 9 books283 ordered entries
- 85 questionstimed practice and rubrics
- 9 deep casesreal operating decisions
Optional: Build a private plan from your role, rounds, available time, and recent evidence. It stays in this browser.
The curriculum
Nine books, ordered from foundations to interview execution.
Open a book to inspect its chapters, or start reading its first entry now.
Core ML
Build the technical base used across applied, research, and engineering interviews.
- I
ML foundations
Math, probability, classical machine learning, deep learning, and the core questions that test them.
9 chapters · 62 entries - II
Model training and research
Optimization, reliable experiments, implementation, debugging, and research judgment.
9 chapters · 52 entries - III
Evaluation and product ML
Metrics, experimental validity, calibration, product decisions, and production evaluation.
4 chapters · 25 entries
Frontier AI systems
Study language models, post-training, agents, accelerators, and distributed systems.
- IV
LLMs, agents, and post-training
Transformer internals, inference, retrieval, evaluation, agents, alignment, and post-training.
7 chapters · 42 entries - V
ML systems and infrastructure
Accelerators, distributed training, inference systems, reliability, cost, and full ML architecture.
6 chapters · 30 entries
Specialist tracks
Add only the specialist subject required by the role and team.
- VI
Retrieval, ranking, and recommendations
Embeddings, candidate generation, ranking, search metrics, cold start, and feedback loops.
4 chapters · 23 entries - VII
Reinforcement learning and robotics
Sequential decisions, value and policy methods, environments, rewards, and robotics policy learning.
3 chapters · 13 entries - VIII
Vision, language, and speech
Visual models, multimodal systems, sequence modeling, natural language, and speech.
4 chapters · 21 entries
Interview execution
Prepare role choice, project evidence, behavioral judgment, and senior-level communication.
- IX
Interview and career practice
Role choice, level calibration, project stories, behavioral judgment, and long-form field guides.
3 chapters · 15 entries
Why mlmentorship
Interview performance, not passive completion.
See the expected depth
Answers separate reliable execution, senior ownership, staff architecture, principal judgment, and company-dependent upper-IC scope.
Practice before reading
Questions include timers and observable rubrics. Deep cases connect models, evidence, systems, failure, and ownership.
Built for AS, RS, MLE, and RE candidates. Generic algorithms, SQL, and backend curricula remain external.
Inspect the depth
All design questions The design cases connect technical choices to evidence and ownership.
Built from practice
Specific, current, and honest about scope.
Written by Hamidreza Saghir, Principal Applied Scientist at Microsoft, with earlier ML engineering, applied-science, and research roles at X, Amazon, and Borealis AI. The site uses public process evidence, never leaked prompts or job-outcome promises. About the author and project.